Using administrative health data to identify individuals with intellectual and developmental disabilities: a comparison of algorithms
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Notice bibliographique
Résumé
BACKGROUND: Individuals with intellectual and developmental disabilities (IDD) experience high rates of physical and mental health problems; yet their health care is often inadequate. Information about their characteristics and health services needs is critical for planning efficient and equitable services. A logical source of such information is administrative health data; however, it can be difficult to identify cases with IDD in these data. The purpose of this study is to evaluate three algorithms for case finding of IDD in health administrative data. METHODS: The three algorithms were created following existing approaches in the literature which ranged between maximising sensitivity versus balancing sensitivity and specificity. The broad algorithm required only one IDD service contact across all available data and time periods, the intermediate algorithm added the restriction of a minimum of two physician visits while the narrow algorithm added a further restriction that the time period be limited to 2006 onward. The resulting three cohorts were compared according to socio-demographic and clinical characteristics. Comparisons on different subgroups for a hypothetical population of 50,000 individuals with IDD were also carried out: this information may be relevant for planning specialised treatment or support programmes. RESULTS: The prevalence rates of IDD per 100 were 0.80, 0.52 and 0.18 for the broad, intermediate and narrow algorithms, respectively. Except for 'percentage with psychiatric co-morbidity', the three cohorts had similar characteristics (standardised differences < 0.1). More stringent thresholds increased the percentage of psychiatric co-morbidity and decreased the percentages of women and urban residents in the identified cohorts (standardised differences = 0.12 to 0.46). More concretely, using the narrow algorithm to indirectly estimate the number of individuals with IDD, a practice not uncommon in planning and policy development, classified nearly 7000 more individuals with psychiatric co-morbidities than using the intermediate algorithm. CONCLUSIONS: The prevalence rate produced by the intermediate algorithm most closely approximated the reported literature rate suggesting the value of imposing a two-physician visit minimum but not restricting the time period covered. While the statistical differences among the algorithms were generally minor, differences in the numbers of individuals in specific population subgroups may be important particularly if they have specific service needs. Health administrative data can be useful for broad-based service planning for individuals with IDD and for population level comparisons around their access and quality of care.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle